R Notebook

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rm(list = ls())

library(Lahman)
library(mosaic)
library(tidyr)
library(tidyverse)
library(dplyr)
library(mplot)
library(ggplot2)
library(cluster)
library(factoextra)
library(corrplot)
library(data.table)
library(mod)
library(modelr)
library(leaps)
library(caret)
library(ISLR2)
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library(ggcorrplot)
library(glmnet)

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library(glmnet)

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rm(list = ls())

library(Lahman)

package 㤼㸱Lahman㤼㸲 was built under R version 4.0.5

library(mosaic)

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library(tidyr)

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library(tidyverse)

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library(dplyr)
library(mplot)

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library(ggplot2)
library(cluster)
library(factoextra)

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package 㤼㸱factoextra㤼㸲 was built under R version 4.0.5Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(corrplot)

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library(data.table)

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library(mod)

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library(modelr)


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#Load in People, Batting, and Pitching Dataframes
data("People") 
data("Batting")
data("Pitching")

#Looking at vars in all data frames 
names(People)

 [1] "playerID"     "birthYear"    "birthMonth"   "birthDay"     "birthCountry" "birthState"  
 [7] "birthCity"    "deathYear"    "deathMonth"   "deathDay"     "deathCountry" "deathState"  
[13] "deathCity"    "nameFirst"    "nameLast"     "nameGiven"    "weight"       "height"      
[19] "bats"         "throws"       "debut"        "finalGame"    "retroID"      "bbrefID"     
[25] "deathDate"    "birthDate"

names(Batting)

 [1] "playerID" "yearID"   "stint"    "teamID"   "lgID"     "G"        "AB"       "R"       
 [9] "H"        "X2B"      "X3B"      "HR"       "RBI"      "SB"       "CS"       "BB"      
[17] "SO"       "IBB"      "HBP"      "SH"       "SF"       "GIDP"

names(Pitching)

 [1] "playerID" "yearID"   "stint"    "teamID"   "lgID"     "W"        "L"        "G"       
 [9] "GS"       "CG"       "SHO"      "SV"       "IPouts"   "H"        "ER"       "HR"      
[17] "BB"       "SO"       "BAOpp"    "ERA"      "IBB"      "WP"       "HBP"      "BK"      
[25] "BFP"      "GF"       "R"        "SH"       "SF"       "GIDP"

#Looking at years 
Pitching%>%
 arrange(yearID)

#Merges player name to Batting data. 
bstats <- battingStats()
    str(bstats)

'data.frame':   108789 obs. of  29 variables:
 $ playerID: chr  "abercda01" "addybo01" "allisar01" "allisdo01" ...
 $ yearID  : int  1871 1871 1871 1871 1871 1871 1871 1871 1871 1871 ...
 $ stint   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ teamID  : Factor w/ 149 levels "ALT","ANA","ARI",..: 136 111 39 142 111 56 111 24 56 24 ...
 $ lgID    : Factor w/ 7 levels "AA","AL","FL",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ G       : int  1 25 29 27 25 12 1 31 1 18 ...
 $ AB      : int  4 118 137 133 120 49 4 157 5 86 ...
 $ R       : int  0 30 28 28 29 9 0 66 1 13 ...
 $ H       : int  0 32 40 44 39 11 1 63 1 13 ...
 $ X2B     : int  0 6 4 10 11 2 0 10 1 2 ...
 $ X3B     : int  0 0 5 2 3 1 0 9 0 1 ...
 $ HR      : int  0 0 0 2 0 0 0 0 0 0 ...
 $ RBI     : int  0 13 19 27 16 5 2 34 1 11 ...
 $ SB      : int  0 8 3 1 6 0 0 11 0 1 ...
 $ CS      : int  0 1 1 1 2 1 0 6 0 0 ...
 $ BB      : int  0 4 2 0 2 0 1 13 0 0 ...
 $ SO      : int  0 0 5 2 1 1 0 1 0 0 ...
 $ IBB     : int  NA NA NA NA NA NA NA NA NA NA ...
 $ HBP     : int  NA NA NA NA NA NA NA NA NA NA ...
 $ SH      : int  NA NA NA NA NA NA NA NA NA NA ...
 $ SF      : int  NA NA NA NA NA NA NA NA NA NA ...
 $ GIDP    : int  0 0 1 0 0 0 0 1 0 0 ...
 $ BA      : num  0 0.271 0.292 0.331 0.325 0.224 0.25 0.401 0.2 0.151 ...
 $ PA      : num  4 122 139 133 122 49 5 170 5 86 ...
 $ TB      : num  0 38 54 64 56 15 1 91 2 17 ...
 $ SlugPct : num  0 0.322 0.394 0.481 0.467 0.306 0.25 0.58 0.4 0.198 ...
 $ OBP     : num  0 0.295 0.302 0.331 0.336 0.224 0.4 0.447 0.2 0.151 ...
 $ OPS     : num  0 0.617 0.696 0.812 0.803 ...
 $ BABIP   : num  0 0.271 0.303 0.326 0.328 0.229 0.25 0.404 0.2 0.151 ...

    

People$name <- paste(People$nameFirst, People$nameLast, sep = " ")

batting_name <- merge(Batting,
                 People[,c("playerID", "name")],
                 by = "playerID", all.x = TRUE)

#Merges player name to Pitching data.

People$name <- paste(People$nameFirst, People$nameLast, sep = " ")

pitching_name <- merge(Pitching,
                 People[,c("playerID", "name")],
                 by = "playerID", all.x = TRUE)

#Creating additional stats for bstats
bstats[is.na(bstats)] = 0
#is.nan(bstats)

bstats <- bstats %>%
  mutate(K_Percent = SO / PA) %>%
  mutate(BB_Percent = (BB + IBB) / PA) %>%
  mutate_all(~replace(., is.nan(.), 0))

invalid factor level, NA generatedinvalid factor level, NA generated

bstats <- bstats %>%
  mutate_at(vars(K_Percent, BB_Percent), funs(round(., 3)))

`funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

bstats_salary <- bstats %>%
              filter(yearID >= 1985) %>%
              left_join(select(Salaries, playerID, yearID, teamID, salary), 
                         by=c("playerID", "yearID", "teamID"))

bstats_salary[is.na(bstats_salary)] = 0
str(bstats_salary)

'data.frame':   46535 obs. of  32 variables:
 $ playerID  : chr  "aasedo01" "abregjo01" "ackerji01" "adamsri02" ...
 $ yearID    : num  1985 1985 1985 1985 1985 ...
 $ stint     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ teamID    : Factor w/ 149 levels "ALT","ANA","ARI",..: 5 35 134 117 33 102 94 134 134 134 ...
 $ lgID      : Factor w/ 7 levels "AA","AL","FL",..: 2 5 2 5 2 5 5 2 2 2 ...
 $ G         : num  54 6 61 54 54 91 22 12 36 14 ...
 $ AB        : num  0 9 0 121 0 165 36 20 0 34 ...
 $ R         : num  0 0 0 12 0 27 1 2 0 2 ...
 $ H         : num  0 0 0 23 0 46 10 4 0 4 ...
 $ X2B       : num  0 0 0 3 0 7 2 1 0 1 ...
 $ X3B       : num  0 0 0 1 0 3 0 0 0 0 ...
 $ HR        : num  0 0 0 2 0 6 0 1 0 0 ...
 $ RBI       : num  0 1 0 10 0 21 2 5 0 3 ...
 $ SB        : num  0 0 0 1 0 1 0 0 0 0 ...
 $ CS        : num  0 0 0 1 0 0 0 0 0 0 ...
 $ BB        : num  0 0 0 5 0 22 1 3 0 0 ...
 $ SO        : num  0 2 0 23 0 26 5 6 0 10 ...
 $ IBB       : num  0 0 0 3 0 5 0 0 0 0 ...
 $ HBP       : num  0 0 0 1 0 6 0 0 0 0 ...
 $ SH        : num  0 0 0 3 0 4 7 0 0 0 ...
 $ SF        : num  0 0 0 0 0 3 0 1 0 0 ...
 $ GIDP      : num  0 0 0 2 0 7 1 1 0 1 ...
 $ BA        : num  0 0 0 0.19 0 0.279 0.278 0.2 0 0.118 ...
 $ PA        : num  0 9 0 130 0 200 44 24 0 34 ...
 $ TB        : num  0 0 0 34 0 77 12 8 0 5 ...
 $ SlugPct   : num  0 0 0 0.281 0 0.467 0.333 0.4 0 0.147 ...
 $ OBP       : num  0 0 0 0.228 0 0.378 0.297 0.292 0 0.118 ...
 $ OPS       : num  0 0 0 0.509 0 0.845 0.63 0.692 0 0.265 ...
 $ BABIP     : num  0 0 0 0.219 0 0.294 0.323 0.214 0 0.167 ...
 $ K_Percent : num  0 0.222 0 0.177 0 0.13 0.114 0.25 0 0.294 ...
 $ BB_Percent: num  0 0 0 0.062 0 0.135 0.023 0.125 0 0 ...
 $ salary    : num  0 0 170000 0 147500 ...

bstats_sure <- bstats_salary %>%
  filter(PA > 150) %>%
  select(OPS, BABIP, K_Percent, BB_Percent, salary)

Data Preparation (Lesson 1 & 2)

#Keep players with over 150 at bats. (We can change this value if necessary).
#Creating batting average variable.

batting1 <- bstats %>%
  filter(AB >= 150)

bstats %>%
  filter(playerID == "bogaexa01")

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Exploratory Analysis (Lesson 1 & 2)

Lessons 1 and 2 will just be parts of the overall project. Simple things like data manipulation, apply functions, boxplots, etc. This will be data preparation items and exploratory analysis.

b <- ggplot(batting1, aes(x = teamID, y = HR)) +
  geom_boxplot(col = "black", aes(fill = teamID))
b

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hitters1 <- batting1 %>%
  filter(yearID < 1895) %>%
  select(SlugPct)

hitters2 <- batting1 %>%
  filter(yearID > 1894, yearID < 1921) %>%
  select(SlugPct)

hitters3 <- batting1 %>%
  filter(yearID > 1920, yearID < 1969) %>%
  select(SlugPct)

hitters4 <- batting1 %>%
  filter(yearID > 1969) %>%
  select(SlugPct)
#Organizing 4 different datasets looking at slugging percentage for the following boxplots. All of these are somewhat different eras, with the most dramatic split being from before 1920 (pre-Babe Ruth) and after 1920 (during and post-Babe Ruth)

boxplot(hitters1,
        main = "Slugging percentage from late 1871 - 1894",
        ylab = "Slugging percentage",
        col = "blue",
        horizontal = TRUE)

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boxplot(hitters2, 
        main = "Slugging percentage from 1895-1920",
        ylab = "Slugging percentage",
        col = "yellow",
        horizontal = TRUE)

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boxplot(hitters3, 
        main = "Slugging percentage from 1921-1968",
        ylab = "Slugging percentage",
        col = "red",
        horizontal = TRUE)

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boxplot(hitters4, 
        main = "Slugging percentage from 1969 - present",
        ylab = "Slugging percentage",
        col = "red",
        horizontal = TRUE)

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sapply(hitters1, mean, na.rm = T)

  SlugPct 
0.3456088

sapply(hitters2, mean, na.rm = T)

 SlugPct 
0.348923

sapply(hitters3, mean, na.rm = T)

  SlugPct 
0.3972127

sapply(hitters4, mean, na.rm = T)

  SlugPct 
0.4088045

#Notice that gigantic increase between hitters2 and hitters3

summary(hitters2)

    SlugPct      
 Min.   :0.1480  
 1st Qu.:0.3003  
 Median :0.3430  
 Mean   :0.3489  
 3rd Qu.:0.3910  
 Max.   :0.8490

summary(hitters3)

    SlugPct      
 Min.   :0.1760  
 1st Qu.:0.3420  
 Median :0.3900  
 Mean   :0.3972  
 3rd Qu.:0.4440  
 Max.   :0.8460

summary(hitters4)

    SlugPct      
 Min.   :0.1730  
 1st Qu.:0.3540  
 Median :0.4040  
 Mean   :0.4088  
 3rd Qu.:0.4580  
 Max.   :0.8630

summary(hitters4)

    SlugPct      
 Min.   :0.1730  
 1st Qu.:0.3540  
 Median :0.4040  
 Mean   :0.4088  
 3rd Qu.:0.4580  
 Max.   :0.8630

#Keep batting stats that we want for pairs.
batting_num <- bstats %>%
  filter(PA >= 150) %>%
  select("BA", 'OBP', 'SlugPct', "SO", "BB", "HR")

careerBatting <- na.omit(bstats)

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Career Batting Stats

careerBatting <- na.omit(bstats)

careerBatting <- careerBatting %>%
  select(playerID, BA, PA, SlugPct, OBP, SO, HR) %>%
  group_by(playerID) %>%
  summarise_all('mean')

careerBatting_num <- careerBatting %>%
  filter(PA >= 150) %>%
  select(BA, PA, SlugPct, OBP, SO, HR)

pairs(careerBatting_num)

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corrmatrix <- cor(batting_num)
corrplot(corrmatrix, method = 'number') #Gives us correlation from pairs graph.

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careerBatting_num1 <- careerBatting_num %>%
  filter(PA > 500)

0-dimensional Reduction (Lesson 4)

Bootstrapping

PCA (Lesson 4)

res <- batting_num %>% prcomp(scale = TRUE)
res

Standard deviations (1, .., p=6):
[1] 1.8624983 1.1955799 0.8163046 0.5272521 0.3234188 0.2296540

Rotation (n x k) = (6 x 6):
               PC1         PC2         PC3         PC4        PC5          PC6
BA      -0.3736490  0.53149382  0.20948811 -0.39409469  0.6134310  0.049063667
OBP     -0.4412694  0.38795844 -0.30295510 -0.06651166 -0.5817204  0.469217735
SlugPct -0.4816546  0.08527252  0.45916589  0.20230952 -0.3441137 -0.624948649
SO      -0.2974863 -0.61917967  0.04176753 -0.71554909 -0.1194610  0.009617743
BB      -0.4043725 -0.14520286 -0.75150469  0.19652707  0.2909420 -0.356888661
HR      -0.4262175 -0.39403532  0.29495049  0.49870136  0.2607132  0.509317820

loadings <- res$rotation
loadings

               PC1         PC2         PC3         PC4        PC5          PC6
BA      -0.3736490  0.53149382  0.20948811 -0.39409469  0.6134310  0.049063667
OBP     -0.4412694  0.38795844 -0.30295510 -0.06651166 -0.5817204  0.469217735
SlugPct -0.4816546  0.08527252  0.45916589  0.20230952 -0.3441137 -0.624948649
SO      -0.2974863 -0.61917967  0.04176753 -0.71554909 -0.1194610  0.009617743
BB      -0.4043725 -0.14520286 -0.75150469  0.19652707  0.2909420 -0.356888661
HR      -0.4262175 -0.39403532  0.29495049  0.49870136  0.2607132  0.509317820

score_mat <- res$x
score_mat

                   PC1           PC2           PC3           PC4           PC5           PC6
    [1,] -2.416723e+00  4.560698e+00  1.443027e+00 -7.333664e-01 -4.234072e-01 -1.762980e-01
    [2,]  1.219725e+00  1.849015e+00  8.201252e-01 -1.952915e-01  3.981469e-01  1.822513e-01
    [3,]  1.474218e+00  7.482123e-01  9.141729e-01  8.396478e-01 -2.439173e-01 -5.416897e-01
    [4,]  6.203888e-01  2.304795e+00  1.057469e+00 -2.640564e-01  2.662469e-01  3.072002e-02
    [5,]  2.943669e+00  3.660013e-01  5.506029e-01  4.155864e-01  5.317398e-01 -3.286813e-02
    [6,]  1.777087e+00  1.308355e+00  8.996221e-01  1.318660e-01  3.669270e-01 -1.200040e-01
    [7,]  1.638294e+00  1.365860e+00  9.687942e-01  2.460817e-01  2.518894e-01 -2.846689e-01
    [8,]  1.277859e+00  1.429171e+00  1.237701e+00  3.160011e-01  2.384888e-01 -3.187137e-01
    [9,]  2.575716e+00  7.738965e-01  5.169331e-01  3.002796e-01  5.345135e-01  1.057655e-01
   [10,]  1.406601e+00  1.626801e+00  1.019207e+00  6.878197e-02  3.105486e-01 -1.622986e-01
   [11,]  2.314598e+00  1.032260e+00  3.284764e-01  1.875309e-01  4.737664e-01  3.377658e-01
   [12,] -2.254698e+00  4.915125e+00  1.951355e+00 -1.215630e+00  1.700315e-01  1.075539e-01
   [13,]  1.714346e+00  1.191014e+00  1.054853e+00  3.211466e-01  2.940316e-01 -2.832409e-01
   [14,]  4.214521e+00 -5.515752e-01  3.050290e-01  7.087223e-01  7.976724e-01  3.980669e-02
   [15,]  2.271122e+00  1.078424e+00  5.187903e-01  1.175299e-01  5.660790e-01  2.481744e-01
   [16,]  7.655863e-01  2.041193e+00  1.014612e+00  1.844765e-02  5.616512e-02 -1.959685e-01
   [17,]  1.520407e+00  1.432379e+00  1.091449e+00  2.042299e-01  2.334932e-01 -3.524935e-01
   [18,]  1.490633e+00  1.291377e+00  6.930873e-01  3.470345e-01  6.483829e-02 -9.133176e-02
   [19,] -1.993183e-01  3.201744e+00  1.191853e+00 -5.892269e-01  4.491340e-01  4.036008e-01
   [20,]  3.770710e-01  2.434379e+00  7.232593e-01 -1.258865e-01  2.812814e-02  1.074989e-01
   [21,]  2.922852e-01  2.390471e+00  1.332478e+00 -8.020927e-02  1.240978e-01 -2.261130e-01
   [22,]  7.804409e-01  2.075181e+00  2.583384e-01 -8.693810e-02  3.394759e-03  4.109240e-01
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  [158,]  7.454876e-01  2.311675e+00  9.709886e-01 -3.586801e-01  4.676539e-01  1.803653e-01
  [159,]  3.140966e+00 -1.743634e-01  3.273602e-01  3.639460e-01  5.143199e-01 -9.705433e-02
  [160,]  2.111663e+00  1.092447e+00  5.673255e-01  4.419451e-02  5.741471e-01  1.651516e-01
  [161,]  2.319455e+00  8.086177e-01  7.777198e-01  3.865898e-01  5.497539e-01 -1.071935e-01
  [162,]  1.545696e+00  1.581049e+00  7.992864e-01 -1.243311e-01  5.378649e-01  1.187910e-01
  [163,]  3.098880e-01  2.733051e+00  1.364830e+00 -3.830859e-01  4.013289e-01 -2.839183e-02
  [164,]  1.053480e+00  2.035085e+00  1.117748e+00 -2.013981e-01  4.824652e-01 -2.245388e-02
  [165,]  3.807886e+00 -5.048465e-01  5.647328e-01  7.574360e-01  5.701567e-01 -3.459630e-01
  [166,]  2.855763e+00  5.998251e-01  4.149993e-01  3.069515e-01  6.704102e-01  1.883833e-01
 [ reached getOption("max.print") -- omitted 35229 rows ]

get_eig(res)

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Screeplot

get_eig(res) %>%
  ggplot(aes(x = 1:6, y = cumulative.variance.percent)) +
  geom_line() +
  geom_point() +
  geom_hline(yintercept = 80) +
  xlab("Principal Component") +
  ylab("Proportion of Variance Explained") +
  ggtitle("Scree Plot of Principal Component for Batting Statistics")

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2 Principal Components: PC1 and PC2

fviz_screeplot(res, main = "Scree Plot")

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Can Identify an elbow in 3.

Biplot

res %>%
  fviz_pca_var(axes = c(1,2),
               col.var = "contrib",
               gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
               repel = TRUE
               )

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Cluster Analysis (Lesson 5)

#NOT COMPLETE!!!!! This was just a test, bstats is way too big.
bstats_best <- bstats %>%
  filter(PA >= 600)

eu_dist <- get_dist(careerBatting_num1, method = 'euclidean')

hc_complete <- hclust(eu_dist, method = 'complete')

plot(hc_complete)

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=======

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Silhouette

res_test <- careerBatting_num1 %>% kmeans(7)
  str(res_test)

List of 9
 $ cluster     : int [1:313] 6 1 4 3 3 3 2 2 4 6 ...
 $ centers     : num [1:7, 1:6] 0.274 0.282 0.28 0.279 0.295 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:7] "1" "2" "3" "4" ...
  .. ..$ : chr [1:6] "BA" "PA" "SlugPct" "OBP" ...
 $ totss       : num 651407
 $ withinss    : num [1:7] 15184 28979 12723 15413 13086 ...
 $ tot.withinss: num 110607
 $ betweenss   : num 540799
 $ size        : int [1:7] 50 102 44 36 33 21 27
 $ iter        : int 3
 $ ifault      : int 0
 - attr(*, "class")= chr "kmeans"

distance <- get_dist(careerBatting_num1, method = "euclidean")
sil <- silhouette(x = res_test$cluster, dist = distance)
summary(sil)

Silhouette of 313 units in 7 clusters from silhouette.default(x = res_test$cluster, dist = distance) :
 Cluster sizes and average silhouette widths:
       50       102        44        36        33        21        27 
0.3520849 0.4329740 0.3000351 0.2682495 0.3614668 0.4120772 0.3306556 
Individual silhouette widths:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.06379  0.21423  0.38618  0.36465  0.51790  0.64837

sil %>% head()

     cluster neighbor  sil_width
[1,]       6        5 0.35570879
[2,]       1        7 0.35948971
[3,]       4        6 0.21976316
[4,]       3        1 0.55018020
[5,]       3        4 0.08291147
[6,]       3        1 0.10924787

fviz_nbclust(careerBatting_num1, hcut, hc_method = "complete", hc_metric = "euclidean", method = "wss")

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=======

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fviz_nbclust(careerBatting_num1, hcut, hc_method = "complete", hc_metric = "euclidean", method = "wss")

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=======

>>>>>>> 79253ef43377bff1d46431c2fb3c1c3a1085f815

##This is to test other values of K for the silhouette method.
res_test1 <- careerBatting_num1 %>% kmeans(10 )
  str(res_test1)

List of 9
 $ cluster     : int [1:313] 10 4 6 3 3 3 8 2 6 10 ...
 $ centers     : num [1:10, 1:6] 0.287 0.278 0.285 0.273 0.269 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:10] "1" "2" "3" "4" ...
  .. ..$ : chr [1:6] "BA" "PA" "SlugPct" "OBP" ...
 $ totss       : num 651407
 $ withinss    : num [1:10] 5561 3421 7533 8158 17850 ...
 $ tot.withinss: num 82705
 $ betweenss   : num 568701
 $ size        : int [1:10] 47 26 32 35 31 29 28 40 25 20
 $ iter        : int 4
 $ ifault      : int 0
 - attr(*, "class")= chr "kmeans"

distance <- get_dist(careerBatting_num1, method="euclidean")
sil <- silhouette(x = res_test1$cluster, dist = distance)
summary(sil)

Silhouette of 313 units in 10 clusters from silhouette.default(x = res_test1$cluster, dist = distance) :
 Cluster sizes and average silhouette widths:
       47        26        32        35        31        29        28        40        25 
0.4123528 0.2235291 0.3095730 0.3662893 0.2348064 0.2444163 0.3084443 0.2360885 0.4169711 
       20 
0.3504059 
Individual silhouette widths:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.07461  0.18155  0.32364  0.31245  0.44921  0.62394

sil %>% head()

     cluster neighbor  sil_width
[1,]      10        6 0.25143884
[2,]       4        8 0.56107068
[3,]       6       10 0.21097598
[4,]       3        2 0.37769870
[5,]       3        6 0.09750601
[6,]       3        4 0.41195414

fviz_silhouette(sil)

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Diana

Linear Regression (Lesson 6)

Linear Regression comparing team payroll and win rate.

teams = as.data.table(Teams)
teams = teams[, .(yearID,
                  lgID = as.character(lgID),
                  teamID = as.character(teamID),
                  franchID = as.character(franchID),
                  Rank, G, W, L, R, ERA, SO,
                  WinPercent = W/(W+L))]

salaries = as.data.table(Salaries)
salaries = salaries[, c("lgID", "teamID", "salary1M") :=
                      list(as.character(lgID), as.character(teamID), salary / 1e6L)]
payroll = salaries[, .(payroll = sum(salary1M)), by=.(teamID, yearID)]
teamPayroll = merge(teams, payroll, by = c("teamID", "yearID"))

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ggplot(data = teamPayroll, aes(x = payroll, y = WinPercent)) + geom_point()  + labs(x = "Payroll (in millions)", y = "Win Percentage") +
  geom_smooth(method = lm, se = FALSE)

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=======

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mod_lm <- lm(data = teamPayroll, WinPercent~payroll)
mod_lm


Call:
lm(formula = WinPercent ~ payroll, data = teamPayroll)

Coefficients:
(Intercept)      payroll  
  0.4796007    0.0003396

summary(mod_lm)


Call:
lm(formula = WinPercent ~ payroll, data = teamPayroll)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.230866 -0.048237 -0.000954  0.049584  0.211074 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.4796007  0.0037895 126.561  < 2e-16 ***
payroll     0.0003396  0.0000512   6.633 5.61e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06714 on 916 degrees of freedom
Multiple R-squared:  0.04583,   Adjusted R-squared:  0.04479 
F-statistic:    44 on 1 and 916 DF,  p-value: 5.611e-11

summary(mod_lm)


Call:
lm(formula = WinPercent ~ payroll, data = teamPayroll)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.230866 -0.048237 -0.000954  0.049584  0.211074 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.4796007  0.0037895 126.561  < 2e-16 ***
payroll     0.0003396  0.0000512   6.633 5.61e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06714 on 916 degrees of freedom
Multiple R-squared:  0.04583,   Adjusted R-squared:  0.04479 
F-statistic:    44 on 1 and 916 DF,  p-value: 5.611e-11

payroll_pred <- teamPayroll %>%
  add_predictions(mod_lm)

payroll_pred %>%
  filter(yearID >= 2010) %>%
  arrange(desc(pred)) %>%
  head(25)

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payroll_pred %>%
  filter(yearID >= 2010) %>%
  arrange(desc(WinPercent)) %>%
  head(25)

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Only five teams are in the top 25 of both payroll and win percentage in the 2010s. These teams are the 2011 Phillies, 2011 Yankees, 2010 Yankees, 2012 Yankees, and 2016 Rangers. This shows that spending the most money doesn’t automatically mean you are getting the best product on the field. ## Simple Linear Regression

Multiple Linear Regression

bstats_salary <- bstats_salary %>%
  filter(PA >= 100) %>%
  filter(salary > 500000)

<<<<<<< HEAD

bstats_salary_21century <- bstats_salary %>%
  filter(yearID >= 2002)

lm_mod <- lm(salary ~ H, HR, data = bstats_salary_21century)
summary(lm_mod)


Call:
lm(formula = salary ~ H, data = bstats_salary_21century, subset = HR)

Residuals:
     Min       1Q   Median       3Q      Max 
-4454703 -1184411  -175489   774007 14030406 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -175015.7    82216.4  -2.129   0.0333 *  
H             39604.4      661.7  59.854   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2299000 on 3982 degrees of freedom
Multiple R-squared:  0.4736,    Adjusted R-squared:  0.4735 
F-statistic:  3583 on 1 and 3982 DF,  p-value: < 2.2e-16

lm_mod_prd <- bstats_salary_21century %>% add_predictions(lm_mod)
lm_mod_prd

=======

lm_mod_prd <- bstats_salary %>% add_predictions(lm_mod)
lm_mod_prd

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full_model <- lm(salary ~., data = bstats_sure)
summary(full_model)


Call:
lm(formula = salary ~ ., data = bstats_sure)

Residuals:
     Min       1Q   Median       3Q      Max 
-6914779 -1878645 -1019496   403743 29613794 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -1794883     277282  -6.473 9.93e-11 ***
OPS           9325457     399105  23.366  < 2e-16 ***
BABIP       -10628363    1053976 -10.084  < 2e-16 ***
K_Percent    -3344230     512360  -6.527 6.95e-11 ***
BB_Percent    7390060     977602   7.559 4.31e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3549000 on 13381 degrees of freedom
Multiple R-squared:  0.08167,   Adjusted R-squared:  0.0814 
F-statistic: 297.5 on 4 and 13381 DF,  p-value: < 2.2e-16

full_model_pred <- bstats_salary_21century %>% add_predictions(full_model)

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prediction from a rank-deficient fit may be misleading

full_model_pred

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Resampling Methods

franchise <- c(`ANA` = "LAA", `ARI` = "ARI", `ATL` = "ATL", 
               `BAL` = "BAL", `BOS` = "BOS", `CAL` = "LAA",
               `CHA` = "CHA", `CHN` = "CHN", `CIN` = "CIN", 
               `CLE` = "CLE", `COL` = "COL", `DET` = "DET", 
               `FLO` = "MIA", `HOU` = "HOU", `KCA` = "KCA", 
               `LAA` = "LAA", `LAN` = "LAN", `MIA` = "MIA", 
               `MIL` = "MIL", `MIN` = "MIN", `ML4` = "MIL", 
               `MON` = "WAS", `NYA` = "NYA", `NYM` = "NYN", 
               `NYN` = "NYN", `OAK` = "OAK", `PHI` = "PHI", 
               `PIT` = "PIT", `SDN` = "SDN", `SEA` = "SEA",
               `SFG` = "SFN", `SFN` = "SFN", `SLN` = "SLN", 
               `TBA` = "TBA", `TEX` = "TEX", `TOR` = "TOR",
               `WAS` = "WAS")

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======= >>>>>>> c01ba71f4c47cfc4b1ae37e8d6b53559b193aeaf <<<<<<< HEAD

# Salary of hitters with best batting avg 
top_battingAVG <- bstats_salary_21century%>%
  select(BA, salary) %>%
  arrange(desc(BA))%>%
  head(1500)

  ggplot(data = top_battingAVG, aes(x = BA, y= salary)) +
    geom_point()+
    geom_smooth(method = lm) +
    labs(title="How Batting AVG affects Salary")

# setting seed to generate a reproducible random sampling
set.seed(123)
 
# defining training control as cross-validation and value of K equal to 10
train_control <- trainControl(method = "cv",
                              number = 10)

# training the model
model <- train(salary ~ OBP, data = bstats_salary_21century,
               method = "lm",
               trControl = train_control)

print(model)

=======

avg_team_salaries <- Salaries %>%
    group_by(yearID, franchise, lgID) %>%
    summarise(salary = mean(salary)/1e6) %>%
    filter(!(franchise == "CLE" & lgID == "NL"))

>>>>>>> 79253ef43377bff1d46431c2fb3c1c3a1085f815

`summarise()` has grouped output by 'yearID', 'franchise'. You can override using the `.groups` argument.

Feature Selection

<<<<<<< HEAD

#Correlation mapping 

#making correlation heat map 
corr_numeric <- round(cor(bstats_salary_numvars), 1)

#plot to visualize the correlations 
ggcorrplot(corr_numeric,
           type = "lower",
           lab = TRUE, 
           lab_size = 2,  
           colors = c("tomato2", "white", "springgreen3"),
           title="Correlogram of batting Data", 
           ggtheme=theme_bw)

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=======

>>>>>>> 79253ef43377bff1d46431c2fb3c1c3a1085f815 ======= >>>>>>> c01ba71f4c47cfc4b1ae37e8d6b53559b193aeaf <<<<<<< HEAD

regfit.full = regsubsets(salary ~., data = bstats_salary_numvars,  nvmax = 13, method="exhaustive")

3  linear dependencies found

Reordering variables and trying again:

summary(regfit.full)

Subset selection object
Call: regsubsets.formula(salary ~ ., data = bstats_salary_numvars, 
    nvmax = 13, method = "exhaustive")
26 Variables  (and intercept)
           Forced in Forced out
G              FALSE      FALSE
AB             FALSE      FALSE
R              FALSE      FALSE
H              FALSE      FALSE
X2B            FALSE      FALSE
X3B            FALSE      FALSE
HR             FALSE      FALSE
RBI            FALSE      FALSE
SB             FALSE      FALSE
CS             FALSE      FALSE
BB             FALSE      FALSE
SO             FALSE      FALSE
IBB            FALSE      FALSE
HBP            FALSE      FALSE
SH             FALSE      FALSE
SF             FALSE      FALSE
GIDP           FALSE      FALSE
BA             FALSE      FALSE
SlugPct        FALSE      FALSE
OBP            FALSE      FALSE
BABIP          FALSE      FALSE
K_Percent      FALSE      FALSE
BB_Percent     FALSE      FALSE
PA             FALSE      FALSE
TB             FALSE      FALSE
OPS            FALSE      FALSE
1 subsets of each size up to 14
Selection Algorithm: exhaustive
          G   AB  R   H   X2B X3B HR  RBI SB  CS  BB  SO  IBB HBP SH  SF  GIDP BA  PA  TB 
1  ( 1 )  " " " " " " " " " " " " " " "*" " " " " " " " " " " " " " " " " " "  " " " " " "
2  ( 1 )  " " " " " " " " " " " " " " "*" " " " " " " " " " " " " " " " " " "  " " " " " "
3  ( 1 )  "*" " " " " " " " " " " " " " " " " " " " " " " " " " " "*" " " " "  " " "*" " "
4  ( 1 )  "*" " " " " " " " " " " " " " " " " " " " " " " "*" " " "*" " " " "  " " "*" " "
5  ( 1 )  "*" " " " " " " " " "*" " " " " " " " " " " " " "*" " " "*" " " " "  " " "*" " "
6  ( 1 )  "*" " " " " " " "*" "*" " " " " " " " " " " " " "*" " " "*" " " " "  " " "*" " "
7  ( 1 )  "*" " " " " " " "*" "*" " " " " " " " " " " " " "*" " " "*" " " " "  " " "*" " "
8  ( 1 )  "*" " " " " " " "*" "*" " " " " " " " " " " " " "*" " " "*" " " "*"  " " "*" " "
9  ( 1 )  "*" " " " " " " "*" "*" " " " " " " " " " " " " "*" " " "*" " " "*"  " " "*" " "
10  ( 1 ) "*" " " " " " " "*" "*" " " " " " " " " " " " " "*" " " "*" " " "*"  " " "*" " "
11  ( 1 ) "*" " " " " " " "*" "*" " " " " "*" "*" " " " " "*" " " "*" " " "*"  " " "*" " "
12  ( 1 ) "*" " " " " " " "*" "*" " " " " "*" "*" " " " " "*" " " "*" " " "*"  " " "*" " "
13  ( 1 ) "*" " " " " " " "*" "*" " " "*" "*" "*" " " "*" "*" " " "*" " " "*"  " " "*" " "
14  ( 1 ) "*" "*" " " " " "*" "*" "*" "*" "*" "*" "*" " " "*" " " "*" " " "*"  " " " " " "
          SlugPct OBP OPS BABIP K_Percent BB_Percent
1  ( 1 )  " "     " " " " " "   " "       " "       
2  ( 1 )  " "     " " " " " "   " "       "*"       
3  ( 1 )  " "     " " " " " "   " "       " "       
4  ( 1 )  " "     " " " " " "   " "       " "       
5  ( 1 )  " "     " " " " " "   " "       " "       
6  ( 1 )  " "     " " " " " "   " "       " "       
7  ( 1 )  " "     " " "*" " "   " "       " "       
8  ( 1 )  " "     " " "*" " "   " "       " "       
9  ( 1 )  "*"     " " " " " "   " "       "*"       
10  ( 1 ) "*"     " " " " " "   "*"       "*"       
11  ( 1 ) "*"     " " " " " "   " "       "*"       
12  ( 1 ) "*"     " " " " " "   "*"       "*"       
13  ( 1 ) "*"     " " " " " "   " "       "*"       
14  ( 1 ) " "     " " "*" "*"   " "       " "